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2.0

Jun 25, 2018
06/18

by
Shai Ben-David

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It is well known that most of the common clustering objectives are NP-hard to optimize. In practice, however, clustering is being routinely carried out. One approach for providing theoretical understanding of this seeming discrepancy is to come up with notions of clusterability that distinguish realistically interesting input data from worst-case data sets. The hope is that there will be clustering algorithms that are provably efficient on such 'clusterable' instances. In other words, hope that...

Topics: Computational Complexity, Computing Research Repository, Learning, Computing Research Repository

Source: http://arxiv.org/abs/1501.00437

1
1.0

Jun 25, 2018
06/18

by
Puneet Agarwal; Gautam Shroff; Sarmimala Saikia; Zaigham Khan

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While analyzing vehicular sensor data, we found that frequently occurring waveforms could serve as features for further analysis, such as rule mining, classification, and anomaly detection. The discovery of waveform patterns, also known as time-series motifs, has been studied extensively; however, available techniques for discovering frequently occurring time-series motifs were found lacking in either efficiency or quality: Standard subsequence clustering results in poor quality, to the extent...

Topics: Databases, Computing Research Repository, Learning, Computing Research Repository

Source: http://arxiv.org/abs/1501.00405

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3.0

Jun 25, 2018
06/18

by
Sang-Pil Kim; Myeong-Sun Gil; Yang-Sae Moon; Hee-Sun Won

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Secure similar document detection (SSDD) identifies similar documents of two parties while each party does not disclose its own sensitive documents to another party. In this paper, we propose an efficient 2-step protocol that exploits a feature selection as the lower-dimensional transformation and presents discriminative feature selections to maximize the performance of the protocol. For this, we first analyze that the existing 1-step protocol causes serious computation and communication...

Topics: Cryptography and Security, Computing Research Repository, Databases, Computing Research Repository

Source: http://arxiv.org/abs/1501.00354

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0.0

Jun 30, 2018
06/18

by
Theodoros Rekatsinas; Xu Chu; Ihab F. Ilyas; Christopher Ré

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We introduce HoloClean, a framework for holistic data repairing driven by probabilistic inference. HoloClean unifies existing qualitative data repairing approaches, which rely on integrity constraints or external data sources, with quantitative data repairing methods, which leverage statistical properties of the input data. Given an inconsistent dataset as input, HoloClean automatically generates a probabilistic program that performs data repairing. Inspired by recent theoretical advances in...

Topics: Databases, Computing Research Repository

Source: http://arxiv.org/abs/1702.00820

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9.0

Jun 30, 2018
06/18

by
Delia Fano Yela; Sebastian Ewert; Derry FitzGerald; Mark Sandler

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Musical source separation methods exploit source-specific spectral characteristics to facilitate the decomposition process. Kernel Additive Modelling (KAM) models a source applying robust statistics to time-frequency bins as specified by a source-specific kernel, a function defining similarity between bins. Kernels in existing approaches are typically defined using metrics between single time frames. In the presence of noise and other sound sources information from a single-frame, however,...

Topics: Sound, Computing Research Repository

Source: http://arxiv.org/abs/1702.02130

1
1.0

Jun 30, 2018
06/18

by
Christos Mousas

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This paper presents the three scripting commands and main functionalities of a novel character animation environment called CHASE. CHASE was developed for enabling inexperienced programmers, animators, artists, and students to animate in meaningful ways virtual reality characters. This is achieved by scripting simple commands within CHASE. The commands identified, which are associated with simple parameters, are responsible for generating a number of predefined motions and actions of a...

Topics: Computing Research Repository, Graphics

Source: http://arxiv.org/abs/1702.03246

2
2.0

Jun 30, 2018
06/18

by
Valeria Villani; Lorenzo Sabattini; Giuseppe Riggio; Alessio Levratti; Cristian Secchi; Cesare Fantuzzi

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In this paper we introduce a novel approach that enables users to interact with a mobile robot in a natural manner. The proposed interaction system does not require any specific infrastructure or device, but relies on commonly utilized objects while leaving the user's hands free. Specifically, we propose to utilize a smartwatch (or a sensorized wristband) for recognizing the motion of the user's forearm. Measurements of accelerations and angular velocities are exploited to recognize user's...

Topics: Computing Research Repository, Robotics

Source: http://arxiv.org/abs/1704.00905

2
2.0

Jun 30, 2018
06/18

by
Alexander C. Woods; Hung M. La

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Unmanned Aerial Vehicles (UAV), commonly known as drones, have many potential uses in real world applications. Drones require advanced planning and navigation algorithms to enable them to safely move through and interact with the world around them. This paper presents an extended potential field controller (ePFC) which enables an aerial robot, or drone, to safely track a dynamic target location while simultaneously avoiding any obstacles in its path. The ePFC outperforms a traditional potential...

Topics: Computing Research Repository, Robotics

Source: http://arxiv.org/abs/1704.04672

1
1.0

Jun 30, 2018
06/18

by
Ashwin Mathur; Fei Han; Hao Zhang

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Recognizing a previously visited place, also known as place recognition (or loop closure detection) is the key towards fully autonomous mobile robots and self-driving vehicle navigation. Augmented with various Simultaneous Localization and Mapping techniques (SLAM), loop closure detection allows for incremental pose correction and can bolster efficient and accurate map creation. However, repeated and similar scenes (perceptual aliasing) and long term appearance changes (e.g. weather variations)...

Topics: Computing Research Repository, Robotics

Source: http://arxiv.org/abs/1704.05215

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0.0

Jun 30, 2018
06/18

by
Benedikt Bitterli; Wenzel Jakob; Jan Novák; Wojciech Jarosz

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We study Markov Chain Monte Carlo (MCMC) methods operating in primary sample space and their interactions with multiple sampling techniques. We observe that incorporating the sampling technique into the state of the Markov Chain, as done in Multiplexed Metropolis Light Transport (MMLT), impedes the ability of the chain to properly explore the path space, as transitions between sampling techniques lead to disruptive alterations of path samples. To address this issue, we reformulate Multiplexed...

Topics: Computing Research Repository, Graphics

Source: http://arxiv.org/abs/1704.06835

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0.0

Jun 30, 2018
06/18

by
Aaron Becker; Chris Ertel; James McLurkin

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Micro- and nanorobotics have the potential to revolutionize many applications including targeted material delivery, assembly, and surgery. The same properties that promise breakthrough solutions---small size and large populations---present unique challenges to generating controlled motion. We want to use large swarms of robots to perform manipulation tasks; unfortunately, human-swarm interaction studies as conducted today are limited in sample size, are difficult to reproduce, and are prone to...

Topics: Robotics, Computing Research Repository

Source: http://arxiv.org/abs/1402.3653

0
0.0

Jun 30, 2018
06/18

by
Murat Kocaoglu; Karthikeyan Shanmugam; Alexandros G. Dimakis; Adam Klivans

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Let $f:\{-1,1\}^n$ be a polynomial with at most $s$ non-zero real coefficients. We give an algorithm for exactly reconstructing f given random examples from the uniform distribution on $\{-1,1\}^n$ that runs in time polynomial in $n$ and $2s$ and succeeds if the function satisfies the unique sign property: there is one output value which corresponds to a unique set of values of the participating parities. This sufficient condition is satisfied when every coefficient of f is perturbed by a small...

Topics: Computing Research Repository, Learning

Source: http://arxiv.org/abs/1402.3902

0
0.0

Jun 30, 2018
06/18

by
Youyi Zheng; Julie Dorsey; Niloy Mitra

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The paper addresses the following problem: given a set of man-made shapes, e.g., chairs, can we quickly rank and explore the set of shapes with respect to a given avatar pose? Answering this question requires identifying which shapes are more suitable for the defined avatar and pose; and moreover, to provide fast preview of how to alter the input geometry to better fit the deformed shapes to the given avatar pose? The problem naturally links physical proportions of human body and its...

Topics: Computing Research Repository, Graphics

Source: http://arxiv.org/abs/1402.5440

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0.0

Jun 30, 2018
06/18

by
Clemens Hage; Tim Habigt; Martin Kleinsteuber

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Sparse signal models are in the focus of recent developments in narrowband DOA estimation. Applying these methods to localizing audio sources, however, is challenging due to the wideband nature of the signals. The common approach of processing all frequency bands separately and fusing the results is costly and can introduce errors in the solution. We show how these problems can be overcome by decomposing the wavefield of a circular microphone array and using circular harmonic coefficients...

Topics: Computing Research Repository, Sound

Source: http://arxiv.org/abs/1403.1501

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0.0

Jun 30, 2018
06/18

by
Rémi Flamary; Nisrine Jrad; Ronald Phlypo; Marco Congedo; Alain Rakotomamonjy

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This work investigates the use of mixed-norm regularization for sensor selection in Event-Related Potential (ERP) based Brain-Computer Interfaces (BCI). The classification problem is cast as a discriminative optimization framework where sensor selection is induced through the use of mixed-norms. This framework is extended to the multi-task learning situation where several similar classification tasks related to different subjects are learned simultaneously. In this case, multi-task learning...

Topics: Computing Research Repository, Learning

Source: http://arxiv.org/abs/1403.3628

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0.0

Jun 30, 2018
06/18

by
Jie Chen; José Carlos M. Bermudez; Cédric Richard

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Non-negative least-mean-square (NNLMS) algorithm and its variants have been proposed for online estimation under non-negativity constraints. The transient behavior of the NNLMS, Normalized NNLMS, Exponential NNLMS and Sign-Sign NNLMS algorithms have been studied in our previous work. In this technical report, we derive closed-form expressions for the steady-state excess mean-square error (EMSE) for the four algorithms. Simulations results illustrate the accuracy of the theoretical results. This...

Topics: Computing Research Repository, Learning

Source: http://arxiv.org/abs/1401.6376

0
0.0

Jun 30, 2018
06/18

by
Helene Lachambre; Benjamin Ricaud; Guillaume Stempfel; Bruno Torresani; Christoph Wiesmeyr; Darian M. Onchis

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This article deals with the use of optimal lattice and optimal window in Discrete Gabor Transform computation. In the case of a generalized Gaussian window, extending earlier contributions, we introduce an additional local window adaptation technique for non-stationary signals. We illustrate our approach and the earlier one by addressing three time-frequency analysis problems to show the improvements achieved by the use of optimal lattice and window: close frequencies distinction, frequency...

Topics: Computing Research Repository, Sound

Source: http://arxiv.org/abs/1403.2180

0
0.0

Jun 30, 2018
06/18

by
Debarghya Ghoshdastidar; Ambedkar Dukkipati; Ajay P. Adsul; Aparna S. Vijayan

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Motivated by multi-distribution divergences, which originate in information theory, we propose a notion of `multi-point' kernels, and study their applications. We study a class of kernels based on Jensen type divergences and show that these can be extended to measure similarity among multiple points. We study tensor flattening methods and develop a multi-point (kernel) spectral clustering (MSC) method. We further emphasize on a special case of the proposed kernels, which is a multi-point...

Topics: Computing Research Repository, Learning

Source: http://arxiv.org/abs/1403.4378

1
1.0

Jun 30, 2018
06/18

by
Adrian Weller; Tony Jebara

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When belief propagation (BP) converges, it does so to a stationary point of the Bethe free energy $F$, and is often strikingly accurate. However, it may converge only to a local optimum or may not converge at all. An algorithm was recently introduced for attractive binary pairwise MRFs which is guaranteed to return an $\epsilon$-approximation to the global minimum of $F$ in polynomial time provided the maximum degree $\Delta=O(\log n)$, where $n$ is the number of variables. Here we...

Topics: Computing Research Repository, Learning

Source: http://arxiv.org/abs/1401.0044

0
0.0

Jun 30, 2018
06/18

by
Nicolas Galichet; Michèle Sebag; Olivier Teytaud

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Motivated by applications in energy management, this paper presents the Multi-Armed Risk-Aware Bandit (MARAB) algorithm. With the goal of limiting the exploration of risky arms, MARAB takes as arm quality its conditional value at risk. When the user-supplied risk level goes to 0, the arm quality tends toward the essential infimum of the arm distribution density, and MARAB tends toward the MIN multi-armed bandit algorithm, aimed at the arm with maximal minimal value. As a first contribution,...

Topics: Computing Research Repository, Learning

Source: http://arxiv.org/abs/1401.1123

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0.0

Jun 30, 2018
06/18

by
Matteo Lasagni; Kay Römer

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We present design and implementation of a chain of particles that can be programmed to fold the chain into a given curve. The particles guide an external force to fold, therefore the particles are simple and amenable for miniaturization. A chain can consist of a large number of such particles. Using multiple of these chains, a shape-shifting display can be constructed that folds its initially flat surface to approximate a given 3D shape that can be touched and modified by users, for example,...

Topics: Robotics, Computing Research Repository

Source: http://arxiv.org/abs/1402.2507

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0.0

Jun 30, 2018
06/18

by
S kumarasawamy; Srikanth P L; Manjula S H; K R Venugopal; L M Patnaik

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Privacy preservation emphasize on authorization of data, which signifies that data should be accessed only by authorized users. Ensuring the privacy of data is considered as one of the challenging task in data management. The generalization of data with varying concept hierarchies seems to be interesting solution. This paper proposes two stage prediction processes on privacy preserved data. The privacy is preserved using generalization and betraying other communicating parties by disguising...

Topics: Databases, Computing Research Repository

Source: http://arxiv.org/abs/1402.7190

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0.0

Jun 30, 2018
06/18

by
Aapo Kyrola; Carlos Guestrin

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We propose a new data structure, Parallel Adjacency Lists (PAL), for efficiently managing graphs with billions of edges on disk. The PAL structure is based on the graph storage model of GraphChi (Kyrola et. al., OSDI 2012), but we extend it to enable online database features such as queries and fast insertions. In addition, we extend the model with edge and vertex attributes. Compared to previous data structures, PAL can store graphs more compactly while allowing fast access to both the...

Topics: Databases, Computing Research Repository

Source: http://arxiv.org/abs/1403.0701

1
1.0

Jun 30, 2018
06/18

by
H. Brendan McMahan

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We present tools for the analysis of Follow-The-Regularized-Leader (FTRL), Dual Averaging, and Mirror Descent algorithms when the regularizer (equivalently, prox-function or learning rate schedule) is chosen adaptively based on the data. Adaptivity can be used to prove regret bounds that hold on every round, and also allows for data-dependent regret bounds as in AdaGrad-style algorithms (e.g., Online Gradient Descent with adaptive per-coordinate learning rates). We present results from a large...

Topics: Computing Research Repository, Learning

Source: http://arxiv.org/abs/1403.3465

0
0.0

Jun 30, 2018
06/18

by
Kostas Alexis

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This technical report addresses the problem of optimal surveillance of the route followed by a dynamic parade using a team of aerial robots. The dynamic parade is considered to take place within an urban environment, it is discretized and at every iteration, the algorithm computes the best possible placing of the aerial robotic team members, subject to their camera model and the occlusions arising from the environment. As the parade route is only as well covered as its least-covered point, the...

Topics: Computing Research Repository, Robotics

Source: http://arxiv.org/abs/1701.00019

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1.0

Jun 30, 2018
06/18

by
Alfredo Cuzzocrea; Jérôme Darmont; Hadj Mahboubi

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XML data sources are more and more gaining popularity in the context of a wide family of Business Intelligence (BI) and On-Line Analytical Processing (OLAP) applications, due to the amenities of XML in representing and managing semi-structured and complex multidimensional data. As a consequence, many XML data warehouse models have been proposed during past years in order to handle hetero-geneity and complexity of multidimensional data in a way traditional relational data warehouse approaches...

Topics: Databases, Computing Research Repository

Source: http://arxiv.org/abs/1701.02190

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0.0

Jun 30, 2018
06/18

by
Marco Gori; Marco Maggini; Alessandro Rossi

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In this document we shows a first implementation and some preliminary results of a new theory, facing Machine Learning problems in the frameworks of Classical Mechanics and Variational Calculus. We give a general formulation of the problem and then we studies basic behaviors of the model on simple practical implementations.

Topics: Learning, Computing Research Repository

Source: http://arxiv.org/abs/1701.02377

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0.0

Jun 30, 2018
06/18

by
Antoine Amarilli; Yael Amsterdamer; Tova Milo; Pierre Senellart

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Many practical scenarios make it necessary to evaluate top-k queries over data items with partially unknown values. This paper considers a setting where the values are taken from a numerical domain, and where some partial order constraints are given over known and unknown values: under these constraints, we assume that all possible worlds are equally likely. Our work is the first to propose a principled scheme to derive the value distributions and expected values of unknown items in this...

Topics: Databases, Computing Research Repository

Source: http://arxiv.org/abs/1701.02634

0
0.0

Jun 30, 2018
06/18

by
Alekh Jindal; Jorge-Arnulfo Quiane-Ruiz; Samuel Madden

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Big data applications have fast arriving data that must be quickly ingested. At the same time, they have specific needs to preprocess and transform the data before it could be put to use. The current practice is to do these preparatory transformations once the data is already ingested, however, this is expensive to run and cumbersome to manage. As a result, there is a need to push data preprocessing down to the ingestion itself. In this paper, we present a declarative data ingestion system,...

Topics: Databases, Computing Research Repository

Source: http://arxiv.org/abs/1701.06093

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0.0

Jun 30, 2018
06/18

by
Gaurav Pandey; Ambedkar Dukkipati

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We propose a neural network based approach for learning topics from text and image datasets. The model makes no assumptions about the conditional distribution of the observed features given the latent topics. This allows us to perform topic modelling efficiently using sentences of documents and patches of images as observed features, rather than limiting ourselves to words. Moreover, the proposed approach is online, and hence can be used for streaming data. Furthermore, since the approach...

Topics: Learning, Computing Research Repository

Source: http://arxiv.org/abs/1701.06796

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1.0

Jun 30, 2018
06/18

by
Yu Sun; Ke Tang; Zexuan Zhu; Xin Yao

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Incremental learning with concept drift has often been tackled by ensemble methods, where models built in the past can be re-trained to attain new models for the current data. Two design questions need to be addressed in developing ensemble methods for incremental learning with concept drift, i.e., which historical (i.e., previously trained) models should be preserved and how to utilize them. A novel ensemble learning method, namely Diversity and Transfer based Ensemble Learning (DTEL), is...

Topics: Learning, Computing Research Repository

Source: http://arxiv.org/abs/1702.03500

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0.0

Jun 30, 2018
06/18

by
Julian Hammer; Jan Eitzinger; Georg Hager; Gerhard Wellein

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Achieving optimal program performance requires deep insight into the interaction between hardware and software. For software developers without an in-depth background in computer architecture, understanding and fully utilizing modern architectures is close to impossible. Analytic loop performance modeling is a useful way to understand the relevant bottlenecks of code execution based on simple machine models. The Roofline Model and the Execution-Cache-Memory (ECM) model are proven approaches to...

Topics: Computing Research Repository, Performance

Source: http://arxiv.org/abs/1702.04653

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0.0

Jun 30, 2018
06/18

by
Robert Griffin; Tyson Cobb; Travis Craig; Mark Daniel; Nick van Dijk; Jeremy Gines; Koen Kramer; Shriya Shah; Olger Siebinga; Jesper Smith; Peter Neuhaus

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Exoskeletons are a promising technology for enabling individuals with mobility limitations to walk again. As the 2016 Cybathlon illustrated, however, we have a considerable way to go before exoskeletons have the necessary capabilities to be incorporated into daily life. Most exoskeletons, power only the hip and knee flexion, whereas we present a new exoskeleton, Mina v2, that includes a powered dorsi/plantar flexion. As our entry to the 2016 Cybathlon Powered Exoskeleton Competition, Mina v2's...

Topics: Computing Research Repository, Robotics

Source: http://arxiv.org/abs/1702.08656

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0.0

Jun 30, 2018
06/18

by
Ali Ghadirzadeh; Atsuto Maki; Danica Kragic; Mårten Björkman

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Skilled robot task learning is best implemented by predictive action policies due to the inherent latency of sensorimotor processes. However, training such predictive policies is challenging as it involves finding a trajectory of motor activations for the full duration of the action. We propose a data-efficient deep predictive policy training (DPPT) framework with a deep neural network policy architecture which maps an image observation to a sequence of motor activations. The architecture...

Topics: Computing Research Repository, Robotics

Source: http://arxiv.org/abs/1703.00727

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1.0

Jun 30, 2018
06/18

by
André Petermann; Martin Junghanns; Erhard Rahm

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Transactional frequent subgraph mining identifies frequent subgraphs in a collection of graphs. This research problem has wide applicability and increasingly requires higher scalability over single machine solutions to address the needs of Big Data use cases. We introduce DIMSpan, an advanced approach to frequent subgraph mining that utilizes the features provided by distributed in-memory dataflow systems such as Apache Spark or Apache Flink. It determines the complete set of frequent subgraphs...

Topics: Databases, Computing Research Repository

Source: http://arxiv.org/abs/1703.01910

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1.0

Jun 30, 2018
06/18

by
Carlos Vega; Paula Roquero; Rafael Leira; Ivan Gonzalez; Javier Aracil

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Nowadays, most systems and applications produce log records that are useful for security and monitoring purposes such as debugging programming errors, checking system status, and detecting configuration problems or even attacks. To this end, a log repository becomes necessary whereby logs can be accessed and visualized in a timely manner. This paper presents Loginson, a high-performance log centralization system for large-scale log collection and processing in large IT infrastructures. Besides...

Topics: Databases, Computing Research Repository

Source: http://arxiv.org/abs/1703.02602

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0.0

Jun 30, 2018
06/18

by
Stephen Giguere; Francisco Garcia; Sridhar Mahadevan

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Although many machine learning algorithms involve learning subspaces with particular characteristics, optimizing a parameter matrix that is constrained to represent a subspace can be challenging. One solution is to use Riemannian optimization methods that enforce such constraints implicitly, leveraging the fact that the feasible parameter values form a manifold. While Riemannian methods exist for some specific problems, such as learning a single subspace, there are more general subspace...

Topics: Learning, Computing Research Repository

Source: http://arxiv.org/abs/1703.02992

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0.0

Jun 30, 2018
06/18

by
Mohammad Emtiyaz Khan; Wu Lin

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Variational inference is computationally challenging in models that contain both conjugate and non-conjugate terms. Methods specifically designed for conjugate models, even though computationally efficient, find it difficult to deal with non-conjugate terms. On the other hand, stochastic-gradient methods can handle the non-conjugate terms but they usually ignore the conjugate structure of the model which might result in slow convergence. In this paper, we propose a new algorithm called...

Topics: Learning, Computing Research Repository

Source: http://arxiv.org/abs/1703.04265

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0.0

Jun 30, 2018
06/18

by
Hossein Hosseini; Yize Chen; Sreeram Kannan; Baosen Zhang; Radha Poovendran

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Advances in Machine Learning (ML) have led to its adoption as an integral component in many applications, including banking, medical diagnosis, and driverless cars. To further broaden the use of ML models, cloud-based services offered by Microsoft, Amazon, Google, and others have developed ML-as-a-service tools as black-box systems. However, ML classifiers are vulnerable to adversarial examples: inputs that are maliciously modified can cause the classifier to provide adversary-desired outputs....

Topics: Learning, Computing Research Repository

Source: http://arxiv.org/abs/1703.04318

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0.0

Jun 30, 2018
06/18

by
Harini Suresh; Peter Szolovits; Marzyeh Ghassemi

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We use autoencoders to create low-dimensional embeddings of underlying patient phenotypes that we hypothesize are a governing factor in determining how different patients will react to different interventions. We compare the performance of autoencoders that take fixed length sequences of concatenated timesteps as input with a recurrent sequence-to-sequence autoencoder. We evaluate our methods on around 35,500 patients from the latest MIMIC III dataset from Beth Israel Deaconess Hospital.

Topics: Learning, Computing Research Repository

Source: http://arxiv.org/abs/1703.07004

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4.0

Jun 30, 2018
06/18

by
Philipp Oettershagen

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Solar power models are a crucial element of solar-powered UAV design and performance analysis. During the conceptual design phase, their accuracy directly relates to the accuracy of the predicted performance metrics and thus the final design characteristics of the solar-powered UAV. Likewise, during the operations phase of a solar-powered UAV accurate solar power income models are required to predict and assess the solar power system performance. However, the existing literature on...

Topics: Computing Research Repository, Robotics

Source: http://arxiv.org/abs/1703.07385

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2.0

Jun 30, 2018
06/18

by
Tuan Le; Spencer Gibb; Hung Manh La; Logan Falk; Tony Berendsen

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Bridge condition assessment is important to maintain the quality of highway roads for public transport. Bridge deterioration with time is inevitable due to aging material, environmental wear and in some cases, inadequate maintenance. Non-destructive evaluation (NDE) methods are preferred for condition assessment for bridges, concrete buildings, and other civil structures. Some examples of NDE methods are ground penetrating radar (GPR), acoustic emission, and electrical resistivity (ER). NDE...

Topics: Computing Research Repository, Robotics

Source: http://arxiv.org/abs/1704.04663

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Jun 30, 2018
06/18

by
Teng Zhang; Kanzhi Wu; Jingwei Song; Shoudong Huang; Gamini Dissanayake

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In this paper, we investigate the convergence and consistency properties of an Invariant-Extended Kalman Filter (RI-EKF) based Simultaneous Localization and Mapping (SLAM) algorithm. Basic convergence properties of this algorithm are proven. These proofs do not require the restrictive assumption that the Jacobians of the motion and observation models need to be evaluated at the ground truth. It is also shown that the output of RI-EKF is invariant under any stochastic rigid body transformation...

Topics: Computing Research Repository, Robotics

Source: http://arxiv.org/abs/1702.06680

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Jun 30, 2018
06/18

by
Caetano Sauer; Goetz Graefe; Theo Härder

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Media failures usually leave database systems unavailable for several hours until recovery is complete, especially in applications with large devices and high transaction volume. Previous work introduced a technique called single-pass restore, which increases restore bandwidth and thus substantially decreases time to repair. Instant restore goes further as it permits read/write access to any data on a device undergoing restore--even data not yet restored--by restoring individual data segments...

Topics: Databases, Computing Research Repository

Source: http://arxiv.org/abs/1702.08042

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Jun 30, 2018
06/18

by
Frank Imeson; Stephen L. Smith

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In this paper we consider discrete robot path planning problems on metric graphs. We propose a clustering method, Gamma-Clustering for the planning graph that significantly reduces the number of feasible solutions, yet retains a solution within a constant factor of the optimal. By increasing the input parameter Gamma, the constant factor can be decreased, but with less reduction in the search space. We provide a simple polynomial- time algorithm for finding optimal Gamma-Clusters, and show that...

Topics: Computing Research Repository, Robotics

Source: http://arxiv.org/abs/1702.08410

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Jun 30, 2018
06/18

by
Raphael Petegrosso; Wei Zhang; Zhuliu Li; Yousef Saad; Rui Kuang

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The success of semi-supervised learning crucially relies on the scalability to a huge amount of unlabelled data that are needed to capture the underlying manifold structure for better classification. Since computing the pairwise similarity between the training data is prohibitively expensive in most kinds of input data, currently, there is no general ready-to-use semi-supervised learning method/tool available for learning with tens of millions or more data points. In this paper, we adopted the...

Topics: Learning, Computing Research Repository

Source: http://arxiv.org/abs/1702.08884

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Jun 30, 2018
06/18

by
Krzysztof Wegner; Tomasz Grajek; Jakub Stankowski; Marek Domanski

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HEVC (MPEG-H Part 2 and H.265) is a new coding technology which is expected to be deployed on the market along with new video services in the near future. HEVC is a successor of currently widely used AVC (MPEG-4 Part 10 and H.264). In this paper, the quality coding gains obtained for the Cascaded Pixel Domain Transcoder of AVC-coded material to HEVC standard are reported. Extensive experiments showed that transcoding with bitrate reduction allows the achievement of better rate-distortion...

Topics: Multimedia, Computing Research Repository

Source: http://arxiv.org/abs/1703.00190

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Jun 30, 2018
06/18

by
Gilad Francis; Lionel Ott; Fabio Ramos

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Safe path planning is a crucial component in autonomous robotics. The many approaches to find a collision free path can be categorically divided into trajectory optimisers and sampling-based methods. When planning using occupancy maps, the sampling-based approach is the prevalent method. The main drawback of such techniques is that the reasoning about the expected cost of a plan is limited to the search heuristic used by each method. We introduce a novel planning method based on trajectory...

Topics: Computing Research Repository, Robotics

Source: http://arxiv.org/abs/1703.00194

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Jun 30, 2018
06/18

by
Zhichen Gong; Huanhuan Chen

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The ubiquity of sequences in many domains enhances significant recent interest in sequence learning, for which a basic problem is how to measure the distance between sequences. Dynamic time warping (DTW) aligns two sequences by nonlinear local warping and returns a distance value. DTW shows superior ability in many applications, e.g. video, image, etc. However, in DTW, two points are paired essentially based on point-to-point Euclidean distance (ED) without considering the autocorrelation of...

Topics: Learning, Computing Research Repository

Source: http://arxiv.org/abs/1703.01141

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Jun 30, 2018
06/18

by
Joshua Achiam; Shankar Sastry

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Exploration in complex domains is a key challenge in reinforcement learning, especially for tasks with very sparse rewards. Recent successes in deep reinforcement learning have been achieved mostly using simple heuristic exploration strategies such as $\epsilon$-greedy action selection or Gaussian control noise, but there are many tasks where these methods are insufficient to make any learning progress. Here, we consider more complex heuristics: efficient and scalable exploration strategies...

Topics: Learning, Computing Research Repository

Source: http://arxiv.org/abs/1703.01732